TrustworthinessAccountabilityTransparencycs.AI
Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems
Michael Papademas, Xenia Ziouvelou, Antonis Troumpoukis, Vangelis Karkaletsis
June 28, 2025
Frontiers Comput. Sci.
3 citations
Key Takeaways
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The trustworthiness gap: Current approaches to assessing trustworthy AI fall into two categories—ethical guidelines that provide holistic perspectives but cannot quantify trustworthiness, and technological tools that can measure specific aspects but lack overall vision. This paper proposes bridging this gap by combining ethical principles with algorithmic methods.
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Reducing subjective bias in AI assessment: The paper identifies self-assessment techniques as a major source of subjectivity in trustworthiness evaluation and proposes using algorithmic criteria (specifically PageRank and TrustRank algorithms) to create more objective, standardized assessment frameworks for AI systems.
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AI's unique societal risk profile: Unlike other technologies, AI's pervasive integration into society and its ability to operate beyond direct human understanding create especially profound societal effects, making trustworthiness assessment particularly urgent and important for responsible deployment.
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Need for comprehensive evaluation frameworks: The paper emphasizes that trustworthy AI requires assessment methods that are simultaneously holistic (addressing multiple ethical dimensions) and quantifiable (providing measurable metrics), rather than choosing between these two approaches.
Abstract
Artificial Intelligence (AI) technology epitomizes the complex challenges posed by human-made artifacts, particularly those widely integrated into society and exerting significant influence, highlighting potential benefits and their negative consequences. While other technologies may also pose substantial risks, AI's pervasive reach makes its societal effects especially profound. The complexity of AI systems, coupled with their remarkable capabilities, can lead to a reliance on technologies that operate beyond direct human oversight or understanding. To mitigate the risks that arise, several theoretical tools and guidelines have been developed, alongside efforts to create technological tools aimed at safeguarding Trustworthy AI. The guidelines take a more holistic view of the issue but fail to provide techniques for quantifying trustworthiness. Conversely, while technological tools are better at achieving such quantification, they lack a holistic perspective, focusing instead on specific aspects of Trustworthy AI. This paper aims to introduce an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank. The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field by introducing algorithmic criteria. The application of our approach indicates that a holistic assessment of an AI system's trustworthiness can be achieved by providing quantitative insights while considering the theoretical content of relevant guidelines.